Impact of low-dose ozone nanobubble treatments on antimicrobial resistance genes in pond water
Bibliographic record
Abstract
Antimicrobial resistance (AMR) poses a significant global health threat as the silent pandemic. Because of the use of antimicrobials in aquaculture systems, fish farms may be potential reservoirs for the dissemination of antimicrobial resistance genes (ARGs). Treatments with disinfectants have been promoted to reduce the use of antibiotics; however, the effect of these types of treatments on AMR or ARGs is not well known. This study aimed to evaluate the effects of low dose ozone treatments (0.15 mg/L) on ARG dynamics in pond water using metagenomic shotgun sequencing analysis. The results suggested that ozone disinfection can increase the relative abundance of acquired ARGs and intrinsic efflux mediated ARGs found in the resistance nodulation cell division (RND) family. Notably, a co-occurrence of efflux and non-efflux ARGs within the same bacterial genera was also observed, with most of these genera dominating the bacterial population following ozone treatments. These findings suggest that ozone treatments may selectively favor the survival of bacterial genera harboring efflux ARGs, which may also have non-efflux ARGs. This study underscores the importance of considering the potential impacts of disinfection practices on AMR gene dissemination particularly in aquaculture settings where disinfectants are frequently used at low levels. Future endeavors should prioritize the evaluation of these strategies, as they may be associated with an increased risk of AMR in aquatic environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".